Anomaly detection in mixed high-dimensional molecular data.

ABSTRACT:

Motivation

Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show at

SUBMITTER: Buck L 

PROVIDER: S-EPMC10457663 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature